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» Using Temporal Data for Making Recommendations
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CCS
2008
ACM
15 years 4 months ago
User-controllable learning of security and privacy policies
Studies have shown that users have great difficulty specifying their security and privacy policies in a variety of application domains. While machine learning techniques have succ...
Patrick Gage Kelley, Paul Hankes Drielsma, Norman ...
KDD
2008
ACM
147views Data Mining» more  KDD 2008»
16 years 2 months ago
Mobile call graphs: beyond power-law and lognormal distributions
We analyze a massive social network, gathered from the records of a large mobile phone operator, with more than a million users and tens of millions of calls. We examine the distr...
Mukund Seshadri, Sridhar Machiraju, Ashwin Sridhar...
CMOT
2010
203views more  CMOT 2010»
14 years 12 months ago
A Bayesian approach to modeling lost person behaviors based on terrain features in Wilderness Search and Rescue
: In Wilderness Search and Rescue (WiSAR), the incident commander (IC) creates a probability distribution map of the likely location of the missing person. This map is important be...
Lanny Lin, Michael A. Goodrich
121
Voted
CIKM
2009
Springer
15 years 9 months ago
Post-rank reordering: resolving preference misalignments between search engines and end users
No search engine is perfect. A typical type of imperfection is the preference misalignment between search engines and end users, e.g., from time to time, web users skip higherrank...
Chao Liu, Mei Li, Yi-Min Wang
CONEXT
2009
ACM
15 years 3 months ago
StarClique: guaranteeing user privacy in social networks against intersection attacks
Building on the popularity of online social networks (OSNs) such as Facebook, social content-sharing applications allow users to form communities around shared interests. Millions...
Krishna P. N. Puttaswamy, Alessandra Sala, Ben Y. ...